import numpy as np

t1 = np.arange(12).reshape((3, 4)).astype(float)

t1[1, 2:] = np.nan

print(t1)
print(np.count_nonzero(t1 != t1))

for i in range(t1.shape[1]):
    temp_col = t1[:, i] #当前的一列
    nan_num = np.count_nonzero(temp_col != temp_col)
    if nan_num != 0: #不为0，说明当前这一列有nan
        temp_not_nan_col = temp_col[temp_col == temp_col] # 当前一列不为null的array

        # 选中当前nan的位置，把值赋值为不为nan的均值
        temp_col[np.isnan(temp_col)] = temp_not_nan_col.mean()

print(t1)

# 常用统计函数
print(t1.max())
print(t1.min())
print(t1.sum(axis=0))
print(t1.mean())
print(np.median(t1))
